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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 17, issue 10
Hydrol. Earth Syst. Sci., 17, 3853–3869, 2013
https://doi.org/10.5194/hess-17-3853-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 17, 3853–3869, 2013
https://doi.org/10.5194/hess-17-3853-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Oct 2013

Research article | 10 Oct 2013

The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps

K. Liechti1, L. Panziera2, U. Germann2, and M. Zappa1 K. Liechti et al.
  • 1Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
  • 2MeteoSwiss, Locarno Monti, Switzerland

Abstract. This study explores the limits of radar-based forecasting for hydrological runoff prediction. Two novel radar-based ensemble forecasting chains for flash-flood early warning are investigated in three catchments in the southern Swiss Alps and set in relation to deterministic discharge forecasts for the same catchments. The first radar-based ensemble forecasting chain is driven by NORA (Nowcasting of Orographic Rainfall by means of Analogues), an analogue-based heuristic nowcasting system to predict orographic rainfall for the following eight hours. The second ensemble forecasting system evaluated is REAL-C2, where the numerical weather prediction COSMO-2 is initialised with 25 different initial conditions derived from a four-day nowcast with the radar ensemble REAL. Additionally, three deterministic forecasting chains were analysed. The performance of these five flash-flood forecasting systems was analysed for 1389 h between June 2007 and December 2010 for which NORA forecasts were issued, due to the presence of orographic forcing.

A clear preference was found for the ensemble approach. Discharge forecasts perform better when forced by NORA and REAL-C2 rather then by deterministic weather radar data. Moreover, it was observed that using an ensemble of initial conditions at the forecast initialisation, as in REAL-C2, significantly improved the forecast skill. These forecasts also perform better then forecasts forced by ensemble rainfall forecasts (NORA) initialised form a single initial condition of the hydrological model. Thus the best results were obtained with the REAL-C2 forecasting chain. However, for regions where REAL cannot be produced, NORA might be an option for forecasting events triggered by orographic precipitation.

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